RT Journal Article
T1 Single and multiple error state-space models for signal extraction
A1 Casals Carro, José
A1 Sotoca López, Sonia
A1 Jerez Méndez, Miguel
AB We compare the results obtained by applying the same signal extraction procedures to two observationally equivalent state-space forms. The first model has different errors affecting the states and the observations, while the second has a single perturbation term which coincides with the one-step-ahead forecast error. The signals extracted from both forms are very similar but their variances are drastically different, because the states for the single-source error representation collapse to exact values while those coming from the multiple-error model remain uncertain. The implications of this result are discussed both, with theoretical arguments and practical examples. We find that single error representations have advantages to compute the likelihood or to adjust for seasonality, while multiple error models are better suited to extract a trend indicator. Building on this analysis, it is natural to adopt a ‘best of both worlds’ approach, which applies each representation to the task in which it has comparative advantage.
PB Taylor & Francis
SN 1563-5163
YR 2013
FD 2013-12-17
LK https://hdl.handle.net/20.500.14352/34237
UL https://hdl.handle.net/20.500.14352/34237
LA eng
NO Ministerio de Economía y Competitividad (MINECO)
DS Docta Complutense
RD 8 abr 2025